There are very few paired high and low resolution images in the real world. The traditional single image Super-Resolution （SR） methods typically use pairs of high-resolution and low-resolution images to train models， but these methods use the way of synthetizing dataset to obtain training set， which only consider bilinear downsampling as image degradation process. However， the image degradation process in the real word is complex and diverse， and traditional image super-resolution methods have poor reconstruction performance when facing real unknown degraded images. Aiming at those problems， a single image super-resolution method was proposed for real complex scenes. Firstly， high- and low-resolution images were captured by the camera with different focal lengths， and these images were registered as image pairs to form a dataset CSR（Camera Super-Resolution dataset） of various scenes. Secondly， to simulate the image degradation process in the real world as much as possible， the image degradation model was improved by the parameter randomization of degradation factors and the nonlinear combination degradation. Besides， the dataset of high- and low-resolution image pairs and the image degradation model were combined to synthetize training set. Finally， as the degradation factors were considered in the dataset， residual shrinkage network and U-Net were embedded into the benchmark model to reduce the redundant information caused by degradation factors in the feature space as much as possible. Experimental results indicate that compared with the BSRGAN （Blind Super-Resolution Generative Adversarial Network） method， under complex degradation conditions， the proposed method improves the PSNR by 0.7 dB and 0.14 dB， and improves SSIM by 0.001 and 0.031 respectively on the RealSR and CSR test sets. The proposed method has better objective indicators and visual effect than the existing methods on complex degradation datasets.

To solve the problems such as low accuracy and poor interpretability of traditional news topic mining, a new method was proposed based on weighted Latent Dirichlet Allocation (LDA) that combined with the information structure characters of the news. Firstly, the vocabulary weights were improved from different angles and the composite weights were built, the more expressive words were got by extending the process of feature items generated by the LDA model. Secondly, the Category Distinguish Word (CDW) method was used to optimize the word order of the generated result, which could reduce the noise and the ambiguity of the topics and improve the interpretability of the topics. Finally, according to the mathematical characteristics of the probability distribution model of the topics, the topics were quantified in terms of the contribution degree from the documents to the topics and the topics weight probability to get the hot topics. The simulation results show that the false negative rate and false positive rate of the weighted LDA model drop by an average of 1.43% and 0.16% compared with the traditional LDA model, and the minimum standard price drops by an average of 2.68%. It confirms the feasibility and effectiveness of this method.